Networks, Data, Models and Statistical Learning in Julia


Re-posted from:

Meetup on 18th November

Simulation has become an important tool to understand real-world phenomena. It is also central to many statistical inference approaches, especially in the increasingly popular Approximate Bayesian Computation (ABC) framework. 

Professor Michael Stumpf will discuss how Julia is starting to allow us to tackle problems in systems medicine and stem cell biology; the analysis of complex networks; and recent applications in financial regulation and policy advice. The Julia programming is particularly suited for problems that are described by big models; i.e. models with many parameters and many constituent parts.

Big models pose much bigger computational and conceptual challenges than Big Data and Julia offers distinct advantages in this context. In discussing these Prof Stumpf will pay particular attention to the uses of Julia in network analysis and computational statistics.

Prof Michael Stumpf holds the Chair in Theoretical Systems Biology at Imperial College London, where his group is primarily concerned with inverse problems in systems and evolutionary biology and complex systems.

In his research he combines a broad range of statistical, mathematical and computational tools to tackle signal transduction and cell-fate decision processes in cell and molecular biology, as well as control of processes on large networks. Much of his work focuses on host-pathogen systems, immune response mechanisms and haematopoiesis and haematopoietic stem cells and their roles in health and disease.

He uses Julia in research and teaching.

Details on the meetup on the Skills Matter site: